Users are turning to online tools to access various types of services. User interactions with the services are often tracked and analyzed. Generally, the analysis is used to improve the services and/or to customize some of the services to the users. For example, interactions of a user with a product's web site are tracked. An interest of the user in a particular product is accordingly estimated. An advertisement for the product is selected based on this interest. Subsequently, when the user returns to the same web site or visits a different web site, the advertisement is inserted in the accessed web site. In this example, tracking and analyzing the user interactions can facilitate targeted advertisement.
Previous techniques for improving various services based on tracked user interactions can be complex. For example, in the context of targeted advertisement, the techniques involve two entities: analysts and marketers. The analysts frequently classify different types of visitors to web sites into buckets or categories, often referred to as segments. The marketers often choose which dimensions or demographics are to be used in segmenting the visitors and the advertisements to be provided to each segment.
However, the analysts and the marketers traditionally use disparate tools. The analysts use analysis and reporting tools. In comparison, the marketers use a digital advertising platform such as a search engine or banner advertisement platform. To provide targeted advertisements, the marketers often need to export and reformat data generated by the analysts into tools such as spreadsheet applications. The marketers then import that data into the digital advertising platform. This process is referred to as “remarketing” in the advertisement industry.
The use of disparate tools results in a complex process of exporting, reformatting, and importing data. In addition to this complexity, the imported data can become stale, impacting the effectiveness of targeted advertisements. More specifically, because of the complexity of the process, there can be a large time gap (e.g., a day) between the time user interactions are tracked and analyzed and the time the resulting data is imported and the targeted advertisement is provided. As such, a user visiting a product's web site and having a particular interest in a product may not receive an advertisement targeted for that product until much later (e.g., the next day). However, the user's interest may have changed during that long time frame (e.g., the user may have bought a similar product from a different web site). Thus, because of the staleness of the data, a lower than desired conversion rate for the target advertisement results meaning that the frequency of the purchase or other desired user action responsive to the targeted advertisement is less than it might otherwise be if the advertisement had been provided more quickly.